The subject matter discussed in this section should not be assumed to be prior art merely as a result of its mention in this section. Similarly, a problem mentioned in this section or associated with the subject matter provided as background should not be assumed to have been previously recognized in the prior art. The subject matter in this section merely represents different approaches, which in and of themselves can also correspond to implementations of the claimed technology.
Autonomous robots have long been the stuff of science fiction fantasy. One technical challenge in realizing the truly autonomous robot is the need for the robot to be able to identify where they are, where they have been and plan where they are going. Traditional SLAM techniques have improved greatly in recent years; however, there remains considerable technical challenge to providing fast accurate and reliable positional awareness to robots and self-guiding mobile platforms.
With the recent proliferation of virtual reality headsets such as the Oculus Rift™ PlayStation™ VR, Samsung Gear™ VR, the HTC Vive™ and others, a new class of devices—one that is not autonomous but rather worn by a human user—that would benefit from fast, accurate and reliable positional information has arisen. Many technical challenges remain however in the field of enabling machines and devices to identify where they are, where they have been and plan where they are going. On especially challenging area involves recognizing a location and obstructions accurately and quickly. A variety of different approaches have been tried. For example RFID/WiFi approaches have proven to be expensive and of limited accuracy. Depth sensor based approaches have been found to be high cost and suffer from power drain and interference issues. Marker based approaches require markers placed within the work area—limiting the useful area in which the device can operate. Visual approaches currently are slow leading to failure when used in fast motion applications. Such approaches can also suffer scale ambiguity. Yet these implementations failed to live up to the standards required for widespread adoption.
The challenge of providing fast reliable affordable positional awareness to devices heretofore remained largely unsolved.